remove more unneeded pure annotations

Author: vtjnash

base/linalg/uniformscaling.jl

@@ -1,7 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 import Base: copy, ctranspose, getindex, show, transpose, one, zero, inv,
-             @_pure_meta, hcat, vcat, hvcat
+             hcat, vcat, hvcat
 import Base.LinAlg: SingularException
 
 struct UniformScaling{T<:Number}
@@ -201,7 +201,7 @@ promote_to_arrays(n,k, ::Type{T}, A, B, C) where {T} =
     (promote_to_arrays_(n[k], T, A), promote_to_arrays_(n[k+1], T, B), promote_to_arrays_(n[k+2], T, C))
 promote_to_arrays(n,k, ::Type{T}, A, B, Cs...) where {T} =
     (promote_to_arrays_(n[k], T, A), promote_to_arrays_(n[k+1], T, B), promote_to_arrays(n,k+2, T, Cs...)...)
-promote_to_array_type(A::Tuple{Vararg{Union{AbstractVecOrMat,UniformScaling}}}) = (@_pure_meta; Matrix)
+promote_to_array_type(A::Tuple{Vararg{Union{AbstractVecOrMat,UniformScaling}}}) = Matrix
 
 for (f,dim,name) in ((:hcat,1,"rows"), (:vcat,2,"cols"))
     @eval begin

base/promotion.jl

@@ -122,11 +122,11 @@ end
 
 ## promotion mechanism ##
 
-promote_type()  = (@_pure_meta; Bottom)
+promote_type()  = Bottom
 promote_type(T) = T
 promote_type(T, S, U, V...) = (@_inline_meta; promote_type(T, promote_type(S, U, V...)))
 
-promote_type(::Type{Bottom}, ::Type{Bottom}) = (@_pure_meta; Bottom)
+promote_type(::Type{Bottom}, ::Type{Bottom}) = Bottom
 promote_type(::Type{T}, ::Type{T}) where {T} = T
 promote_type(::Type{T}, ::Type{Bottom}) where {T} = T
 promote_type(::Type{Bottom}, ::Type{T}) where {T} = T
@@ -162,7 +162,7 @@ function promote_type(::Type{T}, ::Type{S}) where {T,S}
 end
 
 # TODO: this is not hyperpure, but the pure anotation seems necessary for inference to work
-promote_rule(::Type{<:Any}, ::Type{<:Any}) = (@_pure_meta; Bottom)
+promote_rule(::Type{<:Any}, ::Type{<:Any}) = Bottom
 
 promote_result(::Type{<:Any},::Type{<:Any},::Type{T},::Type{S}) where {T,S} = (@_inline_meta; promote_type(T,S))
 # If no promote_rule is defined, both directions give Bottom. In that

base/sparse/abstractsparse.jl

@@ -20,4 +20,4 @@ issparse(S::LinAlg.UnitLowerTriangular{<:Any,<:AbstractSparseMatrix}) = true
 issparse(S::UpperTriangular{<:Any,<:AbstractSparseMatrix}) = true
 issparse(S::LinAlg.UnitUpperTriangular{<:Any,<:AbstractSparseMatrix}) = true
 
-indtype(S::AbstractSparseArray{<:Any,Ti}) where {Ti} = (Base.@_pure_meta; Ti)
+indtype(S::AbstractSparseArray{<:Any,Ti}) where {Ti} = Ti

base/sparse/sparsevector.jl

@@ -2,7 +2,7 @@
 
 ### Common definitions
 
-import Base: scalarmax, scalarmin, sort, find, findnz, @_pure_meta
+import Base: scalarmax, scalarmin, sort, find, findnz
 import Base.LinAlg: promote_to_array_type, promote_to_arrays_
 
 ### The SparseVector
@@ -962,8 +962,8 @@ function hvcat(rows::Tuple{Vararg{Int}}, X::_SparseConcatGroup...)
 end
 
 # make sure UniformScaling objects are converted to sparse matrices for concatenation
-promote_to_array_type(A::Tuple{Vararg{Union{_SparseConcatGroup,UniformScaling}}}) = (@_pure_meta; SparseMatrixCSC)
-promote_to_array_type(A::Tuple{Vararg{Union{_DenseConcatGroup,UniformScaling}}}) = (@_pure_meta; Matrix)
+promote_to_array_type(A::Tuple{Vararg{Union{_SparseConcatGroup,UniformScaling}}}) = SparseMatrixCSC
+promote_to_array_type(A::Tuple{Vararg{Union{_DenseConcatGroup,UniformScaling}}}) = Matrix
 promote_to_arrays_(n::Int, ::Type{SparseMatrixCSC}, J::UniformScaling) = sparse(J, n, n)
 
 # Concatenations strictly involving un/annotated dense matrices/vectors should yield dense arrays